Trains a model given two Input Annotators of types TOKEN and WORD_EMBEDDINGS,
coming from ChunkTokenizer and ChunkEmbeddings Annotators

The returned EnsembleEntityResolverModel consists of two layers:
- First a TFIDF + OvrLogRegClassifier on top of the TOKEN Annotations
- Second a set of ChunkEntityResolversModels, one per each different class from the first layer

This approach allows Spark NLP's Entity Resolution Architecture to scale to a few millions of rows [codes]